Streaming feature selection using alpha-investing

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2005, Pages 384-393

  Zhou, Jing ^a   Stine, Robert ^b   Foster, Dean ^b   Ungar, Lyle ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

Classification; False Discovery Rate; Feature Selection; Multiple Regression

Indexed keywords

CLASSIFICATION; FALSE DISCOVERY RATE; FEATURE SELECTION; MULTIPLE REGRESSION;

CLASSIFICATION (OF INFORMATION); COMPUTATIONAL COMPLEXITY; NUMBER THEORY; PROBLEM SOLVING;

REGRESSION ANALYSIS;

EID: 32344431516     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1081870.1081914     Document Type: Conference Paper

References (23)

1. Special issue on variable selection. In Journal of Machine Learning Research (JMLR), 2003.
2. F. Abramovich, Y. Benjamini, D. Donoho, and I. Johnstone. Adapting to unknown sparsity by controlling the false discovery rate. Technical Report 2000-19, Dept. of Statistics, Stanford University, Stanford, CA, 2000.
3. H. Akaike. Information theory and an extension of the maximum likelihood principle. In B. N. Petrov and F. Csàki, editors, 2nd International Symposium on Information Theory, pages 261-281, Budapest, 1973. Akad. Kiàdo.
4. Y. Benjamini and Y. Hochberg. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B(57):289-300, 1995.
5. P. Bickel and K. Doksum. Mathematical Statistics, Prentice Hall, 2001.
6. D. L. Donoho and I. M. Johnstone. Ideal spatial adaptation by wavelet shrinkage. Biometrika, 81:425-455, 1994.
7. S. Dzeroski and N. Lavrac. Relational Data Mining. Springer-Verlag, 2001.
8. S. Dzeroski, L. D. Raedt, and S. Wrobel. Multi-relational data mining workshop. In KDD-2003, 2003.
9. D. P. Foster and E. I. George. The risk inflation criterion for multiple regression. Annals of Statistics, 22:1947-1975, 1994.
10. D. P. Foster and R. A. Stine. Adaptive variable selection competes with Bayes experts. Submitted for publication, 2004.
11. D. P. Foster and R. A. Stine. Variable selection in data mining: Building a predictive model for bankruptcy. Journal of the American Statistical Association (JASA), 2004. 303-313.
12. E. I. George. The variable selection problem. Journal of the Amer. Statist. Assoc., 95:1304-1308, 2000.
13. E. I. George and D. P. Foster. Calibration and empirical bayes variable selection. Biometrika, 87:731-747, 2000.
14. I. Guyon. Nips 2003 workshop on feature extraction. In http://clopinet.com/isabelle/Projects/NIPS2003/, 2003.
15. J. Jacod and A. Shiryaev. Limit Theorems for Stochastic Processes. Springer-Verlag, NY, 2002.
16. D. Jensen and L. Getoor. IJCAI Workshop on Learning Statistical Models from Relational Data. 2003.
17. I. M. Johnstone and B. W. Silverman. Needles and straw in haystacks: Empirical bayes estimates of possibly sparse sequences. Annals of Statistics, 32:1594-1649, 2004.
18. A. Popescul and L. H. Ungar. Structural logistic regression for link analysis. In KDD Workshop on Multi-Relational Data Mining, 2003.
19. A. Popescul and L. H. Ungar. Cluster-based concept invention for statistical relational learning. In Proc. Conference Knowledge Discovery and Data Mining (KDD-2004), 2004.
20. J. Rissanen. Hypothesis selection and testing by the mdl principle. The Computer Journal, 42:260-269, 1999.
21. G. Schwartz. Estimating the dimension of a model. The Annals of Statistics, 6(2):461-464, 1978.
22. R. A. Stine. Model selection using information theory and the mdl principle. Submitted for publication, 2003.
23. L. H. Ungar, J. Zhou, D. P. Foster, and R. A. Stine. Streaming feature selection using iic. In AI&STAT'05, 2005.